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The study proposed a model for monitoring temperature anomalies in industrial machinery based on image and thermography data, focusing on motors and electrical panels. Enhance the operational stability of the machine through multimodal data fusion analysis using Convolutional Neural Networks (CNN). The model includes four parts: data collection, data preprocessing, and anomaly detection model training and deployment.
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